Investigation of true high frequency electrical substrates of fMRI-based resting state networks using parallel independent component analysis of simultaneous EEG/FMRI data

Sreenath P. Kyathanahally, Yun Wang, Vince D. Calhoun, Gopikrishna Deshpande

Research output: Contribution to journalArticle

Abstract

Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained fromfMRI are correlated with smoothed and down sampled versions of various EEGfeatures such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100ms. Based on the scale free properties of EEG microstates and their correlation with resting state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained fromfMRI more meaningful to typically occurring fast neuronal processes in the sub-100ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling (TR = 200ms) using multiband EPI (MBEPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEGto fMRI temporal resolution. Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.

Original languageEnglish (US)
Article number74
JournalFrontiers in Neuroinformatics
Volume11
DOIs
StatePublished - Dec 22 2017
Externally publishedYes

Keywords

  • Default mode network
  • Neurophysiological basis of DMN
  • Parallel independent component analysis
  • Primary visual cortex
  • Resting state brain networks
  • Simultaneous EEG-fMRI

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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